Advanced statistics project - Fever Treatment Analysis Problem 1 A research laboratory was developing a new compound for the relief of severe cases

# Advanced statistics project - Fever Treatment Analysis...

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Fever Treatment Analysis: Problem 1: A research laboratory was developing a new compound for the relief of severe cases of hay fever. In an experiment with 36 volunteers, the amounts of the two active ingredients (A & B) in the compound were varied at three levels each. Randomization was used in assigning four volunteers to each of the nine treatments. The data on hours of relief can be found in the following .csv file: Fever.csv Sample of the dataset: 1.1) State the Null and Alternate Hypothesis for conducting one-way ANOVA for both the variables ‘A’ and ‘B’ individually. 𝐻 0: The means of A and B treatment variable is equal. 𝐻 1: At least one of the means of A treatment with respect to B treatment is unequal.
Pvalue =1.0 is larger than the level of significance 0.05 in both the A and B treatment individually. Since the p value in this scenario is greater than (0.05), we can say that we fail to reject the Null Hypothesis ( 𝐻 0 ) in both the A and B treatment individually. Thus the mean difference between the A and B treatment are equal. 1.2) Perform one- way ANOVA for variable ‘A’ with respect to the variable ‘Relief’. State whether the Null Hypothesis is accepted or rejected based on the ANOVA results. 𝐻 0: The means of 'Relief' variable with respect to A treatment is equal. 𝐻 1: At least one of the means of 'Relief' variable with respect to A treatment is unequal. Since the p value in this scenario is less than (0.05), we can say that we reject the Null Hypothesis ( 𝐻 0 ). 1.3) Perform one- way ANOVA for variable ‘B’ with respect to the variable ‘Relief’. State whether the Null Hypothesis is acce pted or rejected based on the ANOVA results. 𝐻 0: The means of 'Relief' variable with respect to B treatment is equal. 𝐻 1: At least one of the means of 'Relief' variable with respect to B treatment is unequal.
Since the p value in this scenario is less than 𝛼 (0.05), we can say that we reject the Null Hypothesis ( 𝐻 0). 1.4) Analyse the effects of one variable on another with the help of an interaction plot. What is an interaction between two treatments? [hint: use the ‘pointplot’ function from the ‘seaborn’ graphical subroutine in Python] As seen from the above two interaction plots, there seems to be very high interaction between 2nd and 3rd Treatment and 1st treatment is high interactive with 2nd and 3rd treatment, Overall interaction among the two treatment variables are high. As seen from the above two interaction plots, there seems to be very high interaction between 2nd and 3rd Treatment and 1st treatment is high interactive with 2nd and 3rd treatment. In the 3rd treatment alone interaction was less. Overall interaction among the two treatment variables is moderate. 1.5) Perform a two-way ANOVA based on the different ingredients (variable ‘A’ & ‘B’) and state your results. 𝐻 0: The means of 'Relief' variable with respect to A and B ingredients is equal.